Dimensionality Reduction Algorithm For Hyperspectral Images Based On Projection Pursuit

Compared with multispectral images, the hyperspectral images have more bands, higher spectral resolution, narrower bandwidth, thus can describe and measure the objects in more detail. But for practical purpose, the dimensionality is too high and the computation burden is too heavy. In this thesis, the dimensionality reduction methods of hyperspectral images are studied in detail. The main contributions are as follows:1. A spectral imaging system, which is based on Liquid-Crystal Tunable Filters (LCTF), is constructed. Lots of experiments with different scene are carried out. The experiment results show that the constructed spectral imaging system can capture effective spectral images, which provides the basis for concrete algorithm research.2. Several dimensionality reduction methods of hyperspectral images are analyzed, and the experiment analyses are given included conventional methods of band selection, adaptive subspace decomposition and Principle Component Analysis (PCA). The experiment results show that the band selection methods may lose lots of interesting information. Using PCA to reduce the dimension of the hyperspectral images can remain most of information. It can in general reflect the global features but may ignore the local feature.3. The application of Projection Pursuit (PP) to dimension reduction of the hyperspectral images is studied. The experiments which use skewness, kurtosis, Chiang's product and Jones moment as the projection index and Genetic Algorithm (GA) as the optimization algorithm are given, and the results show that PP can make the local feature of the spectral data prominent, but it can not realize the real-time processing.4. A new method to reduce the dimension of the hyperspectral images is presented, which combines the adaptive subspace decomposition method and Sequential Projection .Pursuit (SPP). The experiment results show that this method can increase the computation speed, and show the local feature further than the conventional projection pursuit, as well as make use of the relativity of the hyperspectral images.